PHYMOS - Proper Hybrid Models for Smarter Vehicles
The vehicle of the future is „smart“.
The ability of a vehicle to respond to a changing environment and changing constraints by adopting its behavior in an optimal way will be considered a commodity feature. Realizing such a flexible behavior in a vehicle requires a high degree of „self-awareness“, in other words the ability to predict the impact of its interaction with the environment. Creating models to describe the vehicle itself and its environment properly in terms of the best trade-off between fidelity and runtime performance in a short period of time and in a very cost effective way is a key success factor.
Classical model-based approaches are typically associated with high development efforts. Advances in the field of artificial intelligence open up new opportunities but depend on large amounts of data, besides other risks to reach a high confidence in the model.
In this project hybrid (data and physics-based) approaches shall be evaluated in concrete applications, aiming to incorporate existing physical knowledge in order to generate scalable “Proper Models” in a very data efficient way. These methods will enable the development and realization of competitive product properties and innovative new functionality for smart vehicles in siginificantly shorter time.
In March 2021 the project started and now there are three years time to work on it.